Security Enhancement in Consumer Enterprises using Neural Nets within the SIEM Framework

With internet popularity increasing every day and more physical activities being brought to the online spectrum, Security Information and Event Management (SIEM) tools have emerged as the main way to tackle any threat intrusions in the network infrastructure of organizations. Threats are evolving and becoming more advanced with the advancements in technology, and detecting and preventing the attacks need more human intervention than ever. To prevent these attacks, artificial intelligence and neural networks are a great way to tackle the increasing needs of human intervention. This article, thus, proposes a novel framework that uses a neural network named FeedForward to develop a model for anomaly and threat protection. It then prompts the user and takes input when a threat is detected. On confirmation of a threat, the feedback is sent back to the neural network to train itself better for future threats and keep itself updated. Therefore, this article proposes a supervised learning and retraining framework on the neural network. The dataset chosen has 763,144 instances that are divided into 80% training and 20% testing dataset. On running the neural network on the dataset, we achieved an astonishing 99.2% accuracy.